Bestbrains Education Academy

Required Skills and Experience General Requirements: Strong proficiency in Python with hands-on experience using NumPy, Pandas, Scikit-learn, and Matplotlib for data analysis, feature engineering, and model development. Deep understanding of Machine Learning algorithms, including regression, classification, recommendation systems, clustering, segmentation, and sequential decision-making for adaptive learning and student performance prediction. Proven experience with Deep Learning frameworks such as TensorFlow, PyTorch, or Keras for model design, training, and deployment. Extensive NLP experience — including text preprocessing, tokenization, embeddings, semantic similarity modeling (Sentence Transformers, spaCy), fuzzy string matching (Levenshtein distance), and Large Language Model (LLM) integration or fine-tuning for conversational AI, grammar correction, and educational dialogue generation. Proficiency in Computer Vision, including object detection and segmentation (e.g., YOLOv8, Mask R-CNN), OCR for handwriting/textbook recognition, and image-based AI applications in education. Experience in building and deploying ML models into production using Docker, FastAPI, Flask, and cloud-based AI endpoints, plus expertise in container orchestration (Kubernetes, EKS/AKS). Strong SQL skills (e.g., PostgreSQL) and experience handling high-volume unstructured data stores (MongoDB, Elasticsearch). Proven experience implementing MLOps pipelines using MLflow or DVC for model versioning and Prometheus/Grafana for real-time monitoring and drift detection. Experience with symbolic math processing libraries (e.g., SymPy) for computational grading and partial credit logic based on equation structure. Experience designing Active Learning and Human-in-the-Loop (HITL) pipelines for data labeling and quality assurance. Knowledge of modern software engineering, version control (Git/GitHub), and data security best practices (AES-256, TLS, FERPA/HIPAA). Excellent problem-solving, analytical, and innovation skills, with the ability to translate learning goals into effective AI-driven teaching and grading solutions. Good to Have: Proven experience deploying cloud-native ML applications on AWS (EKS, S3, SQS) or Azure (AKS, Blob Storage). Experience with conversational AI systems, dialogue management, or intelligent tutoring chatbots. Knowledge of data pipelines and ETL processes using Airflow, Spark, or Pandas. Exposure to LLM fine-tuning and prompt engineering for personalized educational content generation. Academic or professional experience in EdTech, language learning, or AI-driven education systems. Education & Experience: Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field. 5–7 years of hands-on experience in AI/ML model development, preferably in NLP, Computer Vision, or EdTech domains. Project-Specific Requirements: Experience developing AOI detection pipelines using YOLOv8 or Mask R-CNN for segmentation and handwriting OCR. Demonstrated ability to implement symbolic math parsing and grading solutions using SymPy or equivalent tools. Hands-on experience designing and deploying Active Learning and HITL data quality pipelines. Strong expertise in Kubernetes orchestration for scalable ML microservices and implementing robust MLOps practices with MLflow/DVC and Prometheus/Grafana monitoring. Familiarity with compliance and security requirements for regulated educational data (FERPA, HIPAA). Prior experience in EdTech or adaptive learning platforms with proven impact on student assessment or tutoring systems.